Abstract
Since similar complex diseases are much alike in clinical symptoms, patients are easily misdiagnosed and mistreated. It is crucial to accurately predict the disease status and identify markers with high sensitivity and specificity for classifying similar complex diseases. Many approaches incorporating network information have been put forward to predict outcomes, but they are not robust because of their low reproducibility. Several pathway-based methods are robust and functionally interpretable. However, few methods characterize the disease-specific states of single samples from the perspective of pathways. In this study, we propose a novel framework, Pathway Activation for Single Sample (PASS), which utilizes the pathway information in a single sample way to better recognize the differences between two similar complex diseases. PASS can mainly be divided into two parts: for each pathway, the extent of perturbation of edges and the statistic difference of genes caused by a single disease sample are quantified; then, a novel method, named as an AUCpath, is applied to evaluate the pathway activation for single samples from the perspective of genes and their interactions. We have applied PASS to two main types of inflammatory bowel disease (IBD) and widely verified the characteristics of PASS. For a new patient, PASS features can be used as the indicators or potential pathway biomarkers to precisely diagnose complex diseases, discover significant features with interpretability and explore changes in the biological mechanisms of diseases.
Highlights
Complex diseases threaten human health and life quality
Genes that are enriched by the same function (GO term) are aggregated through a flux balance model, and functional modules that maximize the distinction between ulcerative colitis (UC) and Crohn's disease (CD) are obtained
Complex diseases are not determined by a single gene, but by the combination of multiple genes, multiple factors, genetics, and the environment, similar complex diseases are more difficult to diagnose due to similar symptoms
Summary
Complex diseases threaten human health and life quality. Similar complex diseases make the early diagnosis of patients more difficult due to similar clinical symptoms. Several methods based on a Pathway Activation for Single Sample single biological network, such as the metabolic network, regulatory network, or protein–protein interaction (PPI) network, have been put forward to aid in disease prediction, diagnosis, prognosis, and so on (Winter et al, 2012; Cun and Fröhlich, 2013). These methods are not robust because of the low reproducibility (Yousefi and Dougherty, 2012; Amar et al, 2015; Choi et al, 2017) that results from the cellular heterogeneity within tissues, the heterogeneity of samples, and errors of measuring technologies. Discovering the involved pathways and quantifying their disorders are of great significance in understanding complex diseases (Bild et al, 2006; Thomas et al, 2008; Markert et al, 2011; Drier et al, 2013)
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